The present invention relates to search engines which generate results with improved relevance by monitoring user actions.
Search engines are designed to explore data communications networks for documents of interest to a given user and then generate listings of results based on those documents identified in that search. The user specifies this interest by inputting a query, expressed as a “keyword” or set of “keywords,” into the search engine. The keywords are then compared with terms from documents previously indexed by the search engine in order to produce a set of matched documents. Finally, before being presented, the matched documents are ranked by employing any number of different algorithms designed to determine the order with which documents might be relevant to the user. The objective is to quickly point the user toward those matched documents with the greatest likelihood of producing satisfaction.
On the internet (a popular, global data communications network) the number of indexed documents has been growing rapidly due predominantly to improvements in technology and the growth in the quantity of information available. Some queries now return millions of matched documents. As a result, the ability of internet search engines to help users identify documents of particular interest to a given query is being hampered. In other words, while internet users have access to an increasing quantity of potentially relevant information, using keyword queries on search engines to identify those documents that produce satisfaction has become more difficult.
Search engines have thus begun employing many strategies in an attempt to combat this problem. Primarily these consist of improving the algorithms that rank the relevancy of matched documents so that, as the quantity of results increases, those most relevant will still be easily accessible at the top of the list. Some of the major strategies consist of focusing on specific vertical segments, using artificial intelligence to perform contextualized searches, employing personalization, leveraging psychographic, demographic and geographic information and mining the search behaviors of previous users. (Using the behavior of previous users to predict the relevancies of documents for future users has been covered by a number of U.S. patents and applications: 2006/0064411 A1 entitled “Search engine using user intent,” U.S. Pat. No. 6,738,764 B2 entitled “Apparatus and method for adaptively ranking search results,” and U.S. Pat. No. 6,370,526 B1 entitled “Self-adaptive method and system for providing a user-preferred ranking order of object sets,” to name a few.)
While all of these strategies, used either singularly or in combination, provide some benefit, they are incomplete for the simple reason that they do not take into account the behavior of the specific user conducting the search at the moment the search is actually being conducted. Consequently, there is a lot of room to adjust and improve the relevancies of matched documents by examining the behavior of the current user and then responding accordingly in real time.
To illustrate, consider a user who submits a query using the keyword “Washington.” Different algorithms, using the strategies mentioned above or potentially others, can be deployed to determine which documents might have the highest probability of being relevant to that specific user for that particular query. By using only information available prior to the submission, however, there is no way of knowing a priori, with any significant degree of certainty, if the user is more interested in “Washington University,” “George Washington,” or, for that matter, “George Washington University.” Thus, while the search engine, employing whatever algorithms and strategies it deems best, attempts to present the results in the most relevant order possible, without additional information from the user there is a substantial chance that the results will be littered with irrelevant documents.
At this point the users' options are limited. They can scan page by page through potentially millions of extraneous matched documents in an attempt to pick out the ones that matter or they can try to identify additional or more specific keywords in an attempt to produce new, and hopefully more relevant, sets of results. Depending on the nature of the search and the ingenuity of the user, this task can often be painstaking and frustrating, if not impossible.
There is therefore a need for a search engine capable of discerning the intent of the specific user currently conducting a search, at the moment that search is being conducted, in order to dynamically modify the search results “on the fly” with the purpose of ranking the matched documents in the most relevant order possible for that user.